{"id":29797661,"url":"https://github.com/saeed-dev2/movie-recommender-collaborative-filtering","last_synced_at":"2026-06-12T16:31:54.022Z","repository":{"id":303767301,"uuid":"1016616756","full_name":"Saeed-dev2/movie-recommender-collaborative-filtering","owner":"Saeed-dev2","description":"A real-world movie recommendation system using collaborative filtering and matrix factorization. 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Built using the **MovieLens 100k dataset**, this project demonstrates how machine learning can be applied to solve the problem of information overload on streaming platforms.\n\n---\n\n## 📌 Table of Contents\n\n- [Overview](#overview)\n- [Dataset](#dataset)\n- [Features](#features)\n- [Getting Started](#getting-started)\n- [Usage](#usage)\n- [Results](#results)\n- [Technologies Used](#technologies-used)\n- [Contributing](#contributing)\n- [License](#license)\n\n---\n\n## 📖 Overview\n\nThis project simulates a real-world scenario where users rate movies, and the system learns from partial data to make personalized recommendations. The core algorithm is based on **collaborative filtering** using **low-rank matrix factorization**, widely used in production systems like Netflix and Amazon.\n\n---\n\n## 🎯 Dataset\n\n- Dataset: [MovieLens 100k (ex8_movies.mat, ex8_movieParams.mat)](https://grouplens.org/datasets/movielens/)\n- Total Movies: 1,682  \n- Total Users: 943  \n- Ratings Scale: 1 to 5  \n- Sparsity: Most users rate only a small subset of available movies\n\n---\n\n## 🚀 Features\n\n- Collaborative filtering cost function (with and without regularization)\n- Analytical and numerical gradient computation\n- Model training with L-BFGS-B optimization\n- Mean normalization of ratings\n- Personalized movie rating predictions\n- Top-N movie recommendations\n\n---\n\n## 🛠️ Getting Started\n\n\n### 📁 Required Files\n\nPlace the following files in your working directory:\n\n- `ex8_movies.mat`\n- `ex8_movieParams.mat`\n- `movie_ids.txt`\n\n\n### ✅ Install Dependencies\n\nMake sure you have Python and the following libraries:\n\n```bash\npip install numpy matplotlib scipy\n```\n\nOr open the Jupyter notebook in **Google Colab** which already includes all dependencies.\n\n---\n\n## 💡 Usage\n\n1. Open `Untitled1.ipynb` in Google Colab or Jupyter Notebook.\n2. Upload the required `.mat` files and `movie_ids.txt`.\n3. Modify `my_ratings` to include your own movie preferences.\n4. 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